Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.
Michael BackenköhlerJoschka GroßVerena WolfAndrea VolkamerPublished in: Journal of chemical information and modeling (2024)
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E (3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.
Keyphrases
- drug discovery
- machine learning
- big data
- electronic health record
- neural network
- protein protein
- molecular dynamics
- artificial intelligence
- protein kinase
- emergency department
- healthcare
- molecular dynamics simulations
- small molecule
- high resolution
- high throughput
- simultaneous determination
- molecularly imprinted
- liquid chromatography